Using Experiment and First-Principles to Assess Electrochemical Windows of Common Solid Electrolytes for Their Application in All Solid-State Lithium Batteries.
Bibliographic record
Abstract
During the last decades, lithium batteries have been developed to power a growing number of portable applications and to meet the needs of an increasingly mobile society. Their industrial and commercial application has always occurred in two successive steps of equal importance: the discovery of new electrode materials and/or electrolytes, followed by their extensive optimisation. A new generation of lithium batteries has been recently developed to meet high expectations in terms of safety, stability and capacity: All-Solid-State Lithium Batteries (ASSLB), where the conventional liquid electrolyte (LiPF6 + EC/DEC) is replaced by a safer and more stable ceramic, polymer or glass solid electrolyte (SE). ASSLB are partly developed with the prospect of using high potential materials as positive electrode and lithium metal as negative electrode. This is only possible through SE stated large electrochemical windows. Nevertheless, values for these electrochemical windows are very divergent in the literature published through the last decades. Recently, several studies have come to specifically decry the frequent overestimation of SE electrochemical stabilities 1,2. Establishing a robust procedure to determine SE real electrochemical windows has become detrimental. Our work is focused on developing a combined theoretical and experimental approach to better assess the electrochemical stability of widely used SE such as Li1.3Al0.3Ti1.7(PO4)3, Li1.5Al0.5Ti1.5(PO4)3 and LiLaTi2O6. In this presentation, we shed light on the importance of selecting the right experimental setup and explore the link between experimental and interpreted thermodynamic results. 1) Y. Tian, T. Shi, W. Richards, J. Li, J. Kim, S-H. Bo, G. Ceder. Energy Environ. Sci., 2017, 10, 1150 2)Z. Zhang, Y. Shao, B. Lotsch, Y-S. Hu, H. Li, J. Janek, L. Nazar, C-W. Nan, J. Maier, M. Armand, L. Chen. Energy Environ. Sci. 2018,11, 1945-1976.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".